Pattern recognition of epileptic EEG
graphoelements with adaptive segmentation,
supervised and unsupervised learning
algorithms
Vladimir Krajca1, Jiri Hozman1, Jitka Mohylová2, Svojmil Petránek3
1Czech Technical University in Prague, Faculty of Biomedical Engineering,
Czech Republic,
vladimir.krajca@fbmi.cvut.cz
2 VŠB-Technical University of Ostrava, Faculty of Electrical Engineering and
Computer Science, Czech Republic,
jitka.mohylova@vsb.cz
3 Hospital Na Bulovce, Dept. Neurology, Prague, ebupetranek@seznam.cz
1
Introduction

The electroencephalogram (EEG) provides markers of brain disturbances
in the field of epilepsy.

In short duration EEG data recordings, the epileptic graphoelements may
not manifest itself.

The visual analysis of lengthy signals is a tedious task. It is necessary to
track the EEG activity on the computer screen and to detect the
epileptiform graphoelements.

The automation of the process is needed.

The EEG wave classification both by supervised and unsupervised
learning algorithms will be compared.

Combination of the above algorithms will be used
2
Aim of study

To show, that artificial neural networks (ANN) exhibit
better precision of classification of EEG
graphoelements, then cluster analysis used perviously

Cluster analysis can be used in preprocessing – in
semi-automatic creation of etalons for learning
classifiers

Etalons can be extracted both manually and
automatically from original EEG recordings – from
segments detected by adaptive segmentation and
described by a feature set from the time, frequency, and
entropic domains.
3
Automatic identification of EEG graphoelements

In different areas of EEG processing, as
– Brain maturation assesemnt of the newborns
– Monitoring and detection of epileptic seizures in adults
computerized analysis of micro- and macrostructure of
EEG is desirable.

EEG microstructure – identification of single
graphoelements and /or frequency bands, EEG bursts,
artefacts, etc.

Macrostructure – trends, detection of significant events,
behavioral states, sleep stages, reveals hidden
information in long-term EEG processing
4
Cluster analysis and adaptive segmentation yield color identification of the
classes. It reflects microstructure (short events).
Temporal profiles reflect macrostructure, classs membership in the course of a
time
5
Macrostructure is reflected in temporal profiles
(example: time scale 15 min/page)
SIGNIFICANT
EVENT (artefact)
SIGNIFICANT EVENT
(epi paroxysms)
6
Cursor in profile (15 min/page) selects event in original EEG
recording (at that position). Example: muscle artefacts (blue color)
ORIGINAL EEG 10s/page
PROFILE (15min/page)
CURSOR
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Example – epi event at cursor position
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Example – epileptic events are reflected in temporal profile
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Adaptive segmentation and identified clusters improve feature extraction and etalons
selection (we can use as a guide segment boundaries and types/classes of segments)
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Cluster analysis

Advantages:
unsupervised learning („push
the button and wait for results“), classes are
ordered according the increasing amplitude of
segment

Disadvantages: classes (clusters) selected by a
computer

Last (red class) can consist of genuine epileptic
spikes, or there can be artefacts
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Learning (supervised) classifers

Advantages:
by supervised learning we can
ourselves decide, which class is the first, second,
etc. We can decide (by teaching) which types of
graphoelements we are looking for. One class
can consist of moving artefacts, which can be
later eliminated

Disadvantages: teaching of classifier and etalons
(prototypes) selection is a tedious work, requiring
a skilled expert.
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Expert in semi-automatic etalons selection

Best compromise between visual and fullautomatized EEG analysis is semi -automatic
method, using both machine learning and
expertise of the physician

As a first, preprocessing step, cluster
analysis is used for etalons extraction: it is
effective, but the classes are created
independently on a user wishes. They can be
inhomogeneous.
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Learning classifier

Teaching is tedious.

Etalons – typical representatives of the desired
classes must be created/selected by a teacher.

Etalons are submitted to classifier during a learning
process. At least 50-100 prototypes/class are
necessary (personal experience)

Manual prototypes selection is time-consuming:
but we can exploit class centers of the clusters for
automatic prototype selection – outliers are edited
by an expert.
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Automatic classification of EEG graphoelements
by a cluster analysis

Efficient, without necessity of learning

Hybrid segments with overlapping classes exhibiting
features of several classes can be misclassified.

No posiibility to influence classification – to specify
uswer defined classes (artefacts in last class etc.)
Clusters are created by „natural“ data structure

Clusters have spheric shape in the feature space, are
formed without the user intervention.
16
Testing the methodology on the real
data

EEG record of patient with the diagnosis
epilepsy (length 31 min , 8 classes)

Both epileptic graphoelements and
impulse artefacts have similar parameters
(features).
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Cluster analysis
Noise/muscle artefacts are
misclassified into blue (6th)
class of impulse artefacts.
See its position in temporal
profiles.
Note the good identification
of continuous impulse
artefacts in 13th channel.
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Cluster analysis
Misclassified „hybrid“
segments exhibiting
features of both classes.
Blue and violet are the
class colors
Fuzzy cluster analysis
might help to improve to
eliminate the hybrid
segments
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Cluster analysis can be used for semiautomatic extraction of etalons from the raw,
original EEG



Typical, representative segments of the cluster
are positioned in feature space near the center
of gravity.
They are typical members of the class (etalons)
of the class, closest to the class center .
Because cluster analysis works relatively quicky,
we have at our disposal the candidates for
etalons . Only minimum effort is needed for final
editing of the etalons set.
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Representative segments , closest to the
center of cluster = etalons for teaching of the
learning classifier (neural network)
Cluster analysis
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Learning classifiers could provide the solution/improvement
to the above mentioned problems. Method:
1.
User specifies what to search for
2.
Realisation is performed by ANN
(artificial neural networks)
3.
Learning by GA (genetic algorithms)
4.
Weights initializing (to avoid local
minimum) - simulated annealing
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ANN 24-12-8



24 inputs - features
12 neurons in hidden
layer (input features
combining , set
empirically – try and
mistake approach
8 outputs (8 classes)
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ANN, 3-layer perceptron
Improvement of cluster
analysis method –
impulse and noisy
artefacts are
distinguished now.
24
ANN, 3-layer perceptron
Classes are
more
homogeneous
now
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How to select etalons?
1.
Expert selects etalons with a mouse
on the computer screen
2.
(semi) automatically by cluster
analysis (minor editing of the
etalons database)
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Example of etalons selection – by mouse within
the range (boundaries) of adaptive segmentation
segments
ANN, etalons selection
ETALON
SPECTRUM
FEATURES
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ETALON SELECTION FROM EEG
2
ANN, etalons selection
1 - etalon selection
2 – etalon identification
(class number entered by
a teacher)
1
3 – click on the etalon –
features histogram (4)
and spectrum (5)
DATABASE EDITING
Parameters are
compared in small
window (6).
Average features and
average spectrum for
each class (7)
4
3
6
5
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7
Epileptic prototypes and artefacts in two
different channels
ANN, summary sheets
31
Visualization – different types of activity can be
identified by a color directly in the real EEG/temporal
profiles under the cursor position
Results visualization
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ANN, etalons selection
33
Features evaluation
ANN - MLP
(3- layer perceptron)
Scatterogram AP- Sigma (amplitude vs.
sigma frequency band)
a
b
c
d
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Conclusion
ANN with genetic algorithm and simulated annealing can
learn to recognize the EG graphoelements much better
than unsupervised learning algorithm. The types of
graphoelements of classes can be specified by an user.
Cluster analysis provides "natural" clusters, it is not
possible to specify, that class number six, for example,
consists of artifacts
Cluster analysis can be used in the first step of
processing - for etalons specification
Adaptive segmentation can be used for manual selection
of etalons from EEG for segment boundaries plotting in
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the graph
SHLUKOVÁ
ANALÝZA
Fuzzy shluková analýza
(algoritmus FCM).
Impulsy chybně
zařazeny do třídy epi
grafoelementů. Práh 0.3
ZLEPŠENÍ HOMOGENITY
DAT.
NETYPICKÉ SEGMENTY
JSOU VYLOUČENY
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Fuzzy shluková analýza - eliminace outliers s
menším členstvím než 0.3
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Pro hodnocení kvality navržených příznaků lze užít histogram
příznaků a spektrum. Z obr. 11 je opět patrné, že třídy č. 5 a 7
(počítáno od nuly) by se měly sloučit.
ANN - MLP
(3-vrtstvý perceptron)
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Ale !!! : Artefakty - mohou spadnout do poslední třídy, stejně jako
pomalá vysokovoltážní aktivita a poškodit přesnost detekce
ARTEFAKTY
39

Automatic classification and
visualization of epileptic EEG by
supervised and unsupervised
algorithms
40
MOTIVACE – vedlejší
paroxysmus
41
Srovnání fuzzy k-NN a Shlukové
analýzy
42
43
44
Cluster
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46
Fuzzy k-NN
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Problems to be solved






Etalons description – features selection
Database of prototypes
Generalization – presented examples based on etalons
extracted from the beginning of the same recording
Robust identification
Optimal MLP structure (number of hidden neurons)
Modern better classifiers inspired by a nature (genetic
algorithms, ant colony optimization,…).
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